As a first step, we built a model of a gene regulatory network consisting of a small number of genes that positively or negatively regulate one another. By computer simulations, we found that a simple network can produce multiple stable states. We also found that a few rules imposed on the network can cause a unidirectional transition of one network state to another. This mimics the unidirectional transition of cell states during cell differentiation. Using extended computer simulations, we are currently further investigating the dynamic behaviors of gene regulator networks. We are also testing the possibility that our computational model can simulate the global gene expression profiles obtained by DNA microarray analysis of differentiating mouse embryonic stem cells. We are currently applying a variety of methods to massive DNA microarray data that we have generated from our transcription factor-manipulation project. As a way to carry out a computer simulation of biological networks, we have recently a developed stochastic modeling method for the expression of a gene regulated by competing transcription factors. It is widely accepted that gene expression regulation is a stochastic event. The common approach for its computer simulation requires detailed information on the interactions of individual molecules, which is often not available for the analyses of biological experiments. As an alternative approach, we employed a more intuitive model to simulate the experimental result, the Markov-chain model, in which a gene is regulated by activators and repressors, which bind the same site in a mutually exclusive manner. We are currently testing whether the method can be applied to other biological problems.